Overview

Dataset statistics

Number of variables16
Number of observations27763
Missing cells14882
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory128.0 B

Variable types

Numeric16

Alerts

total_mdd_time is highly correlated with m6_mdd_timeHigh correlation
m6_p_earning_days is highly correlated with m6_con_rise_days and 6 other fieldsHigh correlation
m6_mdd_time is highly correlated with total_mdd_timeHigh correlation
m6_con_rise_days is highly correlated with m6_p_earning_days and 4 other fieldsHigh correlation
year_p_earning_days is highly correlated with m6_p_earning_days and 1 other fieldsHigh correlation
m3_p_earning_days is highly correlated with m6_p_earning_days and 5 other fieldsHigh correlation
m3_con_rise_days is highly correlated with m6_p_earning_days and 4 other fieldsHigh correlation
m3_con_fall_days is highly correlated with m3_n_earning_daysHigh correlation
y1_p_earning_days is highly correlated with m6_p_earning_days and 4 other fieldsHigh correlation
m3_n_earning_days is highly correlated with m6_p_earning_days and 5 other fieldsHigh correlation
total_con_fall_days is highly correlated with total_n_earning_daysHigh correlation
total_n_earning_days is highly correlated with total_con_fall_days and 1 other fieldsHigh correlation
total_p_earning_days is highly correlated with total_n_earning_daysHigh correlation
y1_n_earning_days is highly correlated with m6_p_earning_days and 2 other fieldsHigh correlation
m6_p_earning_days is highly correlated with year_p_earning_days and 6 other fieldsHigh correlation
m6_con_rise_days is highly correlated with m3_con_rise_daysHigh correlation
year_p_earning_days is highly correlated with m6_p_earning_days and 6 other fieldsHigh correlation
m3_p_earning_days is highly correlated with m6_p_earning_days and 7 other fieldsHigh correlation
m3_con_rise_days is highly correlated with m6_con_rise_days and 1 other fieldsHigh correlation
m3_con_fall_days is highly correlated with m3_n_earning_days and 1 other fieldsHigh correlation
y1_p_earning_days is highly correlated with m6_p_earning_days and 6 other fieldsHigh correlation
m3_n_earning_days is highly correlated with m6_p_earning_days and 7 other fieldsHigh correlation
total_n_earning_days is highly correlated with m6_p_earning_days and 6 other fieldsHigh correlation
total_p_earning_days is highly correlated with m6_p_earning_days and 6 other fieldsHigh correlation
y1_n_earning_days is highly correlated with m6_p_earning_days and 7 other fieldsHigh correlation
total_mdd_time is highly correlated with m6_mdd_timeHigh correlation
m6_p_earning_days is highly correlated with m6_con_rise_days and 3 other fieldsHigh correlation
m6_mdd_time is highly correlated with total_mdd_timeHigh correlation
m6_con_rise_days is highly correlated with m6_p_earning_days and 3 other fieldsHigh correlation
m3_p_earning_days is highly correlated with m6_p_earning_days and 3 other fieldsHigh correlation
m3_con_rise_days is highly correlated with m6_p_earning_days and 3 other fieldsHigh correlation
m3_con_fall_days is highly correlated with m3_n_earning_daysHigh correlation
y1_p_earning_days is highly correlated with m6_p_earning_days and 2 other fieldsHigh correlation
m3_n_earning_days is highly correlated with m3_p_earning_days and 2 other fieldsHigh correlation
total_con_fall_days is highly correlated with total_n_earning_daysHigh correlation
total_n_earning_days is highly correlated with total_con_fall_days and 1 other fieldsHigh correlation
total_p_earning_days is highly correlated with total_n_earning_daysHigh correlation
y1_n_earning_days is highly correlated with y1_p_earning_daysHigh correlation
m6_p_earning_days is highly correlated with m6_con_rise_days and 9 other fieldsHigh correlation
m6_mdd_time is highly correlated with m3_con_fall_days and 2 other fieldsHigh correlation
m6_con_rise_days is highly correlated with m6_p_earning_days and 4 other fieldsHigh correlation
year_p_earning_days is highly correlated with m6_p_earning_days and 9 other fieldsHigh correlation
m3_p_earning_days is highly correlated with m6_p_earning_days and 9 other fieldsHigh correlation
m3_con_rise_days is highly correlated with m6_p_earning_days and 6 other fieldsHigh correlation
m3_con_fall_days is highly correlated with m6_p_earning_days and 8 other fieldsHigh correlation
y1_p_earning_days is highly correlated with m6_p_earning_days and 9 other fieldsHigh correlation
m3_n_earning_days is highly correlated with m6_p_earning_days and 8 other fieldsHigh correlation
total_n_earning_days is highly correlated with m6_p_earning_days and 8 other fieldsHigh correlation
total_p_earning_days is highly correlated with m6_p_earning_days and 7 other fieldsHigh correlation
y1_n_earning_days is highly correlated with m6_p_earning_days and 9 other fieldsHigh correlation
total_mdd_time has 800 (2.9%) missing values Missing
m6_p_earning_days has 1313 (4.7%) missing values Missing
m6_mdd_time has 2336 (8.4%) missing values Missing
m6_con_rise_days has 1309 (4.7%) missing values Missing
year_p_earning_days has 597 (2.2%) missing values Missing
m3_p_earning_days has 684 (2.5%) missing values Missing
m3_con_rise_days has 698 (2.5%) missing values Missing
m3_con_fall_days has 698 (2.5%) missing values Missing
y1_p_earning_days has 2544 (9.2%) missing values Missing
m3_n_earning_days has 705 (2.5%) missing values Missing
total_con_fall_days has 317 (1.1%) missing values Missing
y1_n_earning_days has 2544 (9.2%) missing values Missing
m6_con_rise_days is highly skewed (γ1 = 25.42038884) Skewed
df_index has unique values Unique
m6_con_rise_days has 3438 (12.4%) zeros Zeros
year_p_earning_days has 1958 (7.1%) zeros Zeros
m3_p_earning_days has 1612 (5.8%) zeros Zeros
m3_con_rise_days has 6342 (22.8%) zeros Zeros
m3_con_fall_days has 7879 (28.4%) zeros Zeros
m3_n_earning_days has 3535 (12.7%) zeros Zeros
total_con_fall_days has 1012 (3.6%) zeros Zeros
total_n_earning_days has 617 (2.2%) zeros Zeros
y1_n_earning_days has 542 (2.0%) zeros Zeros

Reproduction

Analysis started2022-02-26 05:19:20.305286
Analysis finished2022-02-26 05:20:16.731475
Duration56.43 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct27763
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36060.18557
Minimum0
Maximum50881
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:17.018659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20265.1
Q129600.5
median36610
Q343780.5
95-th percentile49400.9
Maximum50881
Range50881
Interquartile range (IQR)14180

Descriptive statistics

Standard deviation9541.487092
Coefficient of variation (CV)0.2645989459
Kurtosis0.8495181619
Mean36060.18557
Median Absolute Deviation (MAD)7090
Skewness-0.7415035061
Sum1001138932
Variance91039975.92
MonotonicityStrictly increasing
2022-02-26T00:20:17.297250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
414051
 
< 0.1%
414161
 
< 0.1%
414151
 
< 0.1%
414141
 
< 0.1%
414131
 
< 0.1%
414121
 
< 0.1%
414111
 
< 0.1%
414101
 
< 0.1%
414091
 
< 0.1%
Other values (27753)27753
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
508811
< 0.1%
508801
< 0.1%
508791
< 0.1%
508781
< 0.1%
508771
< 0.1%
508761
< 0.1%
508751
< 0.1%
508741
< 0.1%
508731
< 0.1%
508721
< 0.1%

ret
Real number (ℝ)

Distinct27145
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite1
Infinite (%)< 0.1%
Meaninf
Minimum-1
Maximuminf
Zeros152
Zeros (%)0.5%
Negative12060
Negative (%)43.4%
Memory size217.0 KiB
2022-02-26T00:20:17.520302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.0763686769
Q1-0.02436475742
median0.004107197864
Q30.03229871349
95-th percentile0.09133561216
Maximuminf
Rangeinf
Interquartile range (IQR)0.0566634709

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)0.02835456829
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2022-02-26T00:20:17.711353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0152
 
0.5%
0.022727272736
 
< 0.1%
-0.041666666675
 
< 0.1%
0.020833333335
 
< 0.1%
0.0044843049334
 
< 0.1%
0.034482758624
 
< 0.1%
0.037037037044
 
< 0.1%
0.0027662517294
 
< 0.1%
0.011627906984
 
< 0.1%
-0.0046511627913
 
< 0.1%
Other values (27135)27572
99.3%
ValueCountFrequency (%)
-11
< 0.1%
-0.77890133921
< 0.1%
-0.55083220871
< 0.1%
-0.39169049211
< 0.1%
-0.38809936451
< 0.1%
-0.3824413711
< 0.1%
-0.37623762381
< 0.1%
-0.37226315791
< 0.1%
-0.33936915891
< 0.1%
-0.31497465411
< 0.1%
ValueCountFrequency (%)
inf1
< 0.1%
1.3120393121
< 0.1%
1.3062730631
< 0.1%
1.2113376431
< 0.1%
0.98794979081
< 0.1%
0.55984555981
< 0.1%
0.44503389331
< 0.1%
0.40316205531
< 0.1%
0.39960822721
< 0.1%
0.39650145771
< 0.1%

total_mdd_time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct313
Distinct (%)1.2%
Missing800
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean379.0711716
Minimum1
Maximum4079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:17.878955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q110
median214
Q3397
95-th percentile1310
Maximum4079
Range4078
Interquartile range (IQR)387

Descriptive statistics

Standard deviation491.4174843
Coefficient of variation (CV)1.29637261
Kurtosis0.5403024179
Mean379.0711716
Median Absolute Deviation (MAD)204
Skewness1.318165848
Sum10220896
Variance241491.1438
MonotonicityNot monotonic
2022-02-26T00:20:18.046982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93325
 
12.0%
13102195
 
7.9%
2742166
 
7.8%
3051033
 
3.7%
31949
 
3.4%
334913
 
3.3%
10889
 
3.2%
91856
 
3.1%
1341593
 
2.1%
8523
 
1.9%
Other values (303)13521
48.7%
(Missing)800
 
2.9%
ValueCountFrequency (%)
1514
 
1.9%
2517
 
1.9%
3278
 
1.0%
4316
 
1.1%
5183
 
0.7%
6106
 
0.4%
7362
 
1.3%
8523
 
1.9%
93325
12.0%
10889
 
3.2%
ValueCountFrequency (%)
40793
 
< 0.1%
29535
 
< 0.1%
23451
 
< 0.1%
22537
 
< 0.1%
188911
 
< 0.1%
185817
0.1%
182716
0.1%
179734
0.1%
179622
0.1%
176638
0.1%

m6_p_earning_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct67
Distinct (%)0.3%
Missing1313
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean5.017429112
Minimum0
Maximum125
Zeros239
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:18.369142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum125
Range125
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.171479143
Coefficient of variation (CV)1.628618753
Kurtosis48.3280023
Mean5.017429112
Median Absolute Deviation (MAD)1
Skewness6.719541114
Sum132711
Variance66.77307139
MonotonicityNot monotonic
2022-02-26T00:20:18.604056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46878
24.8%
55639
20.3%
35609
20.2%
22986
10.8%
62803
10.1%
11335
 
4.8%
7377
 
1.4%
0239
 
0.9%
5350
 
0.2%
6434
 
0.1%
Other values (57)500
 
1.8%
(Missing)1313
 
4.7%
ValueCountFrequency (%)
0239
 
0.9%
11335
 
4.8%
22986
10.8%
35609
20.2%
46878
24.8%
55639
20.3%
62803
10.1%
7377
 
1.4%
101
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
1253
< 0.1%
1242
< 0.1%
1211
 
< 0.1%
772
< 0.1%
762
< 0.1%
753
< 0.1%
741
 
< 0.1%
731
 
< 0.1%
721
 
< 0.1%
714
< 0.1%

m6_mdd_time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct102
Distinct (%)0.4%
Missing2336
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean47.96527313
Minimum1
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:18.849452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median31
Q361
95-th percentile153
Maximum215
Range214
Interquartile range (IQR)56

Descriptive statistics

Standard deviation50.33914023
Coefficient of variation (CV)1.04949137
Kurtosis1.655289342
Mean47.96527313
Median Absolute Deviation (MAD)29
Skewness1.39774565
Sum1219613
Variance2534.029039
MonotonicityNot monotonic
2022-02-26T00:20:19.061634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
313786
13.6%
613244
11.7%
12708
9.8%
302002
 
7.2%
51603
 
5.8%
21582
 
5.7%
921258
 
4.5%
31214
 
4.4%
61074
 
3.9%
91960
 
3.5%
Other values (92)5996
21.6%
(Missing)2336
 
8.4%
ValueCountFrequency (%)
12708
9.8%
21582
5.7%
31214
4.4%
4540
 
1.9%
51603
5.8%
61074
 
3.9%
72
 
< 0.1%
138
 
< 0.1%
146
 
< 0.1%
151
 
< 0.1%
ValueCountFrequency (%)
215258
0.9%
214204
0.7%
21315
 
0.1%
21247
 
0.2%
184319
1.1%
18369
 
0.2%
18216
 
0.1%
18155
 
0.2%
1802
 
< 0.1%
1711
 
< 0.1%

m6_con_rise_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct18
Distinct (%)0.1%
Missing1309
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean2.61907462
Minimum0
Maximum125
Zeros3438
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:19.219316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile6
Maximum125
Range125
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.36567791
Coefficient of variation (CV)0.9032495261
Kurtosis1223.894275
Mean2.61907462
Median Absolute Deviation (MAD)1
Skewness25.42038884
Sum69285
Variance5.596431972
MonotonicityNot monotonic
2022-02-26T00:20:19.355541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
27833
28.2%
36937
25.0%
43491
12.6%
03438
12.4%
11892
 
6.8%
51524
 
5.5%
6845
 
3.0%
7458
 
1.6%
817
 
0.1%
95
 
< 0.1%
Other values (8)14
 
0.1%
(Missing)1309
 
4.7%
ValueCountFrequency (%)
03438
12.4%
11892
 
6.8%
27833
28.2%
36937
25.0%
43491
12.6%
51524
 
5.5%
6845
 
3.0%
7458
 
1.6%
817
 
0.1%
95
 
< 0.1%
ValueCountFrequency (%)
1253
 
< 0.1%
1201
 
< 0.1%
1061
 
< 0.1%
741
 
< 0.1%
483
 
< 0.1%
141
 
< 0.1%
112
 
< 0.1%
102
 
< 0.1%
95
 
< 0.1%
817
0.1%

year_p_earning_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct105
Distinct (%)0.4%
Missing597
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean5.169586984
Minimum0
Maximum245
Zeros1958
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:19.495571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile9
Maximum245
Range245
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.93959228
Coefficient of variation (CV)2.3095834
Kurtosis91.51893328
Mean5.169586984
Median Absolute Deviation (MAD)2
Skewness8.830582264
Sum140437
Variance142.5538639
MonotonicityNot monotonic
2022-02-26T00:20:19.659746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14228
15.2%
33796
13.7%
23741
13.5%
43369
12.1%
52746
9.9%
62543
9.2%
01958
7.1%
71894
6.8%
81284
 
4.6%
9655
 
2.4%
Other values (95)952
 
3.4%
(Missing)597
 
2.2%
ValueCountFrequency (%)
01958
7.1%
14228
15.2%
23741
13.5%
33796
13.7%
43369
12.1%
52746
9.9%
62543
9.2%
71894
6.8%
81284
 
4.6%
9655
 
2.4%
ValueCountFrequency (%)
2451
 
< 0.1%
2442
< 0.1%
2432
< 0.1%
1441
 
< 0.1%
1361
 
< 0.1%
1292
< 0.1%
1261
 
< 0.1%
1251
 
< 0.1%
1243
< 0.1%
1232
< 0.1%

m3_p_earning_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct40
Distinct (%)0.1%
Missing684
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean2.670298017
Minimum0
Maximum61
Zeros1612
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:19.819779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum61
Range61
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.017983527
Coefficient of variation (CV)1.504694795
Kurtosis46.94328397
Mean2.670298017
Median Absolute Deviation (MAD)1
Skewness6.482534628
Sum72309
Variance16.14419162
MonotonicityNot monotonic
2022-02-26T00:20:20.032664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
29188
33.1%
38005
28.8%
15760
20.7%
41934
 
7.0%
01612
 
5.8%
2679
 
0.3%
2970
 
0.3%
2754
 
0.2%
2551
 
0.2%
2848
 
0.2%
Other values (30)278
 
1.0%
(Missing)684
 
2.5%
ValueCountFrequency (%)
01612
 
5.8%
15760
20.7%
29188
33.1%
38005
28.8%
41934
 
7.0%
52
 
< 0.1%
61
 
< 0.1%
102
 
< 0.1%
111
 
< 0.1%
124
 
< 0.1%
ValueCountFrequency (%)
616
 
< 0.1%
433
 
< 0.1%
422
 
< 0.1%
411
 
< 0.1%
401
 
< 0.1%
393
 
< 0.1%
375
 
< 0.1%
364
 
< 0.1%
3517
0.1%
3414
0.1%

m3_con_rise_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing698
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1.730980972
Minimum0
Maximum61
Zeros6342
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:20.221572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum61
Range61
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.573411398
Coefficient of variation (CV)0.9089709384
Kurtosis444.9442815
Mean1.730980972
Median Absolute Deviation (MAD)1
Skewness12.06608929
Sum46849
Variance2.475623428
MonotonicityNot monotonic
2022-02-26T00:20:20.370279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
28373
30.2%
06342
22.8%
15098
18.4%
34809
17.3%
42203
 
7.9%
5104
 
0.4%
771
 
0.3%
649
 
0.2%
86
 
< 0.1%
616
 
< 0.1%
Other values (3)4
 
< 0.1%
(Missing)698
 
2.5%
ValueCountFrequency (%)
06342
22.8%
15098
18.4%
28373
30.2%
34809
17.3%
42203
 
7.9%
5104
 
0.4%
649
 
0.2%
771
 
0.3%
86
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
616
 
< 0.1%
112
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%
86
 
< 0.1%
771
 
0.3%
649
 
0.2%
5104
 
0.4%
42203
7.9%
34809
17.3%

m3_con_fall_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing698
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1.265102531
Minimum0
Maximum11
Zeros7879
Zeros (%)28.4%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:20.521612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.178636713
Coefficient of variation (CV)0.9316531144
Kurtosis4.774458054
Mean1.265102531
Median Absolute Deviation (MAD)1
Skewness1.483998006
Sum34240
Variance1.389184501
MonotonicityNot monotonic
2022-02-26T00:20:20.645756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
18951
32.2%
07879
28.4%
27048
25.4%
32534
 
9.1%
4229
 
0.8%
7162
 
0.6%
6138
 
0.5%
599
 
0.4%
813
 
< 0.1%
97
 
< 0.1%
Other values (2)5
 
< 0.1%
(Missing)698
 
2.5%
ValueCountFrequency (%)
07879
28.4%
18951
32.2%
27048
25.4%
32534
 
9.1%
4229
 
0.8%
599
 
0.4%
6138
 
0.5%
7162
 
0.6%
813
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
104
 
< 0.1%
97
 
< 0.1%
813
 
< 0.1%
7162
 
0.6%
6138
 
0.5%
599
 
0.4%
4229
 
0.8%
32534
 
9.1%
27048
25.4%

y1_p_earning_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct115
Distinct (%)0.5%
Missing2544
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean9.692255839
Minimum0
Maximum245
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:20.793245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median7
Q39
95-th percentile11
Maximum245
Range245
Interquartile range (IQR)3

Descriptive statistics

Standard deviation16.29161715
Coefficient of variation (CV)1.680890128
Kurtosis47.66377097
Mean9.692255839
Median Absolute Deviation (MAD)1
Skewness6.761665085
Sum244429
Variance265.4167895
MonotonicityNot monotonic
2022-02-26T00:20:20.954792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74728
17.0%
84711
17.0%
93720
13.4%
63565
12.8%
52431
8.8%
101899
6.8%
41398
 
5.0%
11687
 
2.5%
3647
 
2.3%
12392
 
1.4%
Other values (105)1041
 
3.7%
(Missing)2544
9.2%
ValueCountFrequency (%)
01
 
< 0.1%
163
 
0.2%
2196
 
0.7%
3647
 
2.3%
41398
 
5.0%
52431
8.8%
63565
12.8%
74728
17.0%
84711
17.0%
93720
13.4%
ValueCountFrequency (%)
2451
 
< 0.1%
2442
 
< 0.1%
2432
 
< 0.1%
1743
< 0.1%
1441
 
< 0.1%
1411
 
< 0.1%
1404
< 0.1%
1396
< 0.1%
1381
 
< 0.1%
1373
< 0.1%

m3_n_earning_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct32
Distinct (%)0.1%
Missing705
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean2.115529603
Minimum0
Maximum40
Zeros3535
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:21.100507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum40
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.25032954
Coefficient of variation (CV)2.009108988
Kurtosis43.23722864
Mean2.115529603
Median Absolute Deviation (MAD)1
Skewness6.493152273
Sum57242
Variance18.0653012
MonotonicityNot monotonic
2022-02-26T00:20:21.250004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
19665
34.8%
29426
34.0%
33761
 
13.5%
03535
 
12.7%
4107
 
0.4%
3560
 
0.2%
2954
 
0.2%
3144
 
0.2%
3443
 
0.2%
2843
 
0.2%
Other values (22)320
 
1.2%
(Missing)705
 
2.5%
ValueCountFrequency (%)
03535
 
12.7%
19665
34.8%
29426
34.0%
33761
 
13.5%
4107
 
0.4%
52
 
< 0.1%
61
 
< 0.1%
101
 
< 0.1%
112
 
< 0.1%
181
 
< 0.1%
ValueCountFrequency (%)
402
 
< 0.1%
397
 
< 0.1%
387
 
< 0.1%
3719
 
0.1%
3631
0.1%
3560
0.2%
3443
0.2%
3326
0.1%
3242
0.2%
3144
0.2%

total_con_fall_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct15
Distinct (%)0.1%
Missing317
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean4.247176273
Minimum0
Maximum43
Zeros1012
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:21.406651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.859886712
Coefficient of variation (CV)0.4379113538
Kurtosis7.715732093
Mean4.247176273
Median Absolute Deviation (MAD)1
Skewness0.5239983841
Sum116568
Variance3.459178581
MonotonicityNot monotonic
2022-02-26T00:20:21.584180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
47450
26.8%
57277
26.2%
34317
15.5%
72973
 
10.7%
21980
 
7.1%
01012
 
3.6%
1898
 
3.2%
6696
 
2.5%
8342
 
1.2%
9336
 
1.2%
Other values (5)165
 
0.6%
(Missing)317
 
1.1%
ValueCountFrequency (%)
01012
 
3.6%
1898
 
3.2%
21980
 
7.1%
34317
15.5%
47450
26.8%
57277
26.2%
6696
 
2.5%
72973
 
10.7%
8342
 
1.2%
9336
 
1.2%
ValueCountFrequency (%)
431
 
< 0.1%
135
 
< 0.1%
1243
 
0.2%
1143
 
0.2%
1073
 
0.3%
9336
 
1.2%
8342
 
1.2%
72973
10.7%
6696
 
2.5%
57277
26.2%

total_n_earning_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct501
Distinct (%)1.8%
Missing234
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean39.48512478
Minimum0
Maximum1885
Zeros617
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:21.776965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median27
Q343
95-th percentile66
Maximum1885
Range1885
Interquartile range (IQR)31

Descriptive statistics

Standard deviation98.51859604
Coefficient of variation (CV)2.495081289
Kurtosis102.6855348
Mean39.48512478
Median Absolute Deviation (MAD)15
Skewness9.39093323
Sum1086986
Variance9705.913766
MonotonicityNot monotonic
2022-02-26T00:20:22.113326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1626
 
2.3%
0617
 
2.2%
11617
 
2.2%
10597
 
2.2%
12586
 
2.1%
13580
 
2.1%
2557
 
2.0%
26538
 
1.9%
9531
 
1.9%
33527
 
1.9%
Other values (491)21753
78.4%
ValueCountFrequency (%)
0617
2.2%
1626
2.3%
2557
2.0%
3475
1.7%
4473
1.7%
5454
1.6%
6476
1.7%
7502
1.8%
8522
1.9%
9531
1.9%
ValueCountFrequency (%)
18851
< 0.1%
18671
< 0.1%
18211
< 0.1%
17631
< 0.1%
17451
< 0.1%
16831
< 0.1%
16471
< 0.1%
16001
< 0.1%
15731
< 0.1%
15561
< 0.1%

total_p_earning_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct546
Distinct (%)2.0%
Missing103
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean48.20195228
Minimum0
Maximum2095
Zeros158
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:22.299291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median33
Q353
95-th percentile88
Maximum2095
Range2095
Interquartile range (IQR)37

Descriptive statistics

Standard deviation109.3305179
Coefficient of variation (CV)2.268176137
Kurtosis99.23915242
Mean48.20195228
Median Absolute Deviation (MAD)18
Skewness9.146921408
Sum1333266
Variance11953.16215
MonotonicityNot monotonic
2022-02-26T00:20:22.456710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14570
 
2.1%
15566
 
2.0%
12563
 
2.0%
11561
 
2.0%
13541
 
1.9%
16536
 
1.9%
17535
 
1.9%
18511
 
1.8%
10491
 
1.8%
19486
 
1.8%
Other values (536)22300
80.3%
ValueCountFrequency (%)
0158
 
0.6%
1409
1.5%
2376
1.4%
3340
1.2%
4391
1.4%
5382
1.4%
6366
1.3%
7345
1.2%
8365
1.3%
9452
1.6%
ValueCountFrequency (%)
20951
< 0.1%
20071
< 0.1%
19991
< 0.1%
19761
< 0.1%
18821
< 0.1%
18771
< 0.1%
18551
< 0.1%
18441
< 0.1%
18061
< 0.1%
17491
< 0.1%

y1_n_earning_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct106
Distinct (%)0.4%
Missing2544
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean7.74539038
Minimum0
Maximum143
Zeros542
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size217.0 KiB
2022-02-26T00:20:22.611629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q37
95-th percentile9
Maximum143
Range143
Interquartile range (IQR)3

Descriptive statistics

Standard deviation16.68447556
Coefficient of variation (CV)2.154116802
Kurtosis43.24684931
Mean7.74539038
Median Absolute Deviation (MAD)1
Skewness6.629690539
Sum195331
Variance278.3717246
MonotonicityNot monotonic
2022-02-26T00:20:22.885926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55110
18.4%
64500
16.2%
44248
15.3%
73138
11.3%
32448
8.8%
81926
 
6.9%
91058
 
3.8%
2919
 
3.3%
0542
 
2.0%
10389
 
1.4%
Other values (96)941
 
3.4%
(Missing)2544
9.2%
ValueCountFrequency (%)
0542
 
2.0%
1287
 
1.0%
2919
 
3.3%
32448
8.8%
44248
15.3%
55110
18.4%
64500
16.2%
73138
11.3%
81926
 
6.9%
91058
 
3.8%
ValueCountFrequency (%)
1431
 
< 0.1%
1422
 
< 0.1%
1403
 
< 0.1%
1394
 
< 0.1%
1382
 
< 0.1%
1376
 
< 0.1%
13612
< 0.1%
1359
< 0.1%
13413
< 0.1%
13322
0.1%

Interactions

2022-02-26T00:20:12.672735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:29.755912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:32.498879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:35.332226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:38.156107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:40.982762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:43.564571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:46.301987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:49.151712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:52.148441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:55.091766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:57.983923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:00.457244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:03.789261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:06.653010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:09.623257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:12.878508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:30.010685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:32.624487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:35.469620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:38.315098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:41.108091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:43.712307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:46.431443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:49.294698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:52.285571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:55.270021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:58.109568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:00.658686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:04.019875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:06.806876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:09.778196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:13.032484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:30.243832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:32.745287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:35.605378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:38.451174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:41.230577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:43.886898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:46.562852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:49.427835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:52.415554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:55.417207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:58.220239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:00.921434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:04.219632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:07.029137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:10.004783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:13.179122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:19:30.495277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-26T00:20:00.314763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:03.505923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:06.511818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:09.478245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-26T00:20:12.411764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-02-26T00:20:23.142451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-26T00:20:23.482326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-26T00:20:23.770475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-26T00:20:24.034276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-26T00:20:15.532809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-26T00:20:15.853355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-26T00:20:16.239468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-26T00:20:16.579314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexrettotal_mdd_timem6_p_earning_daysm6_mdd_timem6_con_rise_daysyear_p_earning_daysm3_p_earning_daysm3_con_rise_daysm3_con_fall_daysy1_p_earning_daysm3_n_earning_daystotal_con_fall_daystotal_n_earning_daystotal_p_earning_daysy1_n_earning_days
000.035794230.066.049.06.09.032.05.05.0133.030.06.0392.0522.0109.0
11-0.003764230.062.049.05.021.031.05.05.0135.026.06.0397.0534.0108.0
22-0.028563230.063.049.05.033.033.05.05.0134.025.06.0408.0546.0109.0
33-0.057333230.065.049.05.042.033.05.07.0135.025.07.0417.0555.0106.0
440.054007230.059.042.05.049.028.05.07.0132.033.07.0430.0562.0108.0
550.025027230.065.051.06.065.032.06.07.0135.028.07.0436.0578.0107.0
660.026036230.070.051.06.079.037.06.04.0136.026.07.0443.0592.0106.0
770.020582230.071.051.06.092.043.06.03.0133.023.07.0453.0605.0109.0
88-0.005967230.071.051.06.0104.039.06.03.0134.026.07.0462.0617.0109.0
99-0.042577230.070.030.06.0112.033.06.04.0135.028.07.0471.0625.0109.0

Last rows

df_indexrettotal_mdd_timem6_p_earning_daysm6_mdd_timem6_con_rise_daysyear_p_earning_daysm3_p_earning_daysm3_con_rise_daysm3_con_fall_daysy1_p_earning_daysm3_n_earning_daystotal_con_fall_daystotal_n_earning_daystotal_p_earning_daysy1_n_earning_days
27753508720.027422274.05.059.04.02.02.01.02.010.02.06.043.063.03.0
2775450873-0.000890274.05.059.03.03.02.02.02.010.02.06.043.064.03.0
2775550874-0.053874274.04.059.02.03.02.02.01.09.02.06.044.064.04.0
2775650875-0.022588274.03.0181.02.03.02.02.02.08.02.06.045.064.05.0
27757508760.019740274.02.0212.02.03.01.01.03.07.03.06.046.064.06.0
27758508770.008026274.03.092.02.04.01.01.03.07.03.06.046.065.06.0
2775950878-0.005621274.04.092.02.05.02.02.02.08.02.06.046.066.05.0
27760508790.017899274.03.092.02.05.02.02.01.07.02.06.047.066.06.0
2776150880-0.073577274.03.092.02.06.03.02.01.07.01.06.047.067.06.0
27762508810.000000274.03.0215.02.00.02.01.01.06.02.06.048.067.07.0